Overview

Dataset statistics

Number of variables14
Number of observations440099
Missing cells645659
Missing cells (%)10.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory50.4 MiB
Average record size in memory120.0 B

Variable types

Numeric8
Categorical6

Warnings

Transaction ID is highly correlated with Date of TravelHigh correlation
Date of Travel is highly correlated with Transaction IDHigh correlation
KM Travelled is highly correlated with Price Charged and 1 other fieldsHigh correlation
Price Charged is highly correlated with KM Travelled and 1 other fieldsHigh correlation
Cost of Trip is highly correlated with KM Travelled and 1 other fieldsHigh correlation
Transaction ID is highly correlated with Date of TravelHigh correlation
Date of Travel is highly correlated with Transaction IDHigh correlation
KM Travelled is highly correlated with Price Charged and 1 other fieldsHigh correlation
Price Charged is highly correlated with KM Travelled and 1 other fieldsHigh correlation
Cost of Trip is highly correlated with KM Travelled and 1 other fieldsHigh correlation
Transaction ID is highly correlated with Date of TravelHigh correlation
Date of Travel is highly correlated with Transaction IDHigh correlation
KM Travelled is highly correlated with Price Charged and 1 other fieldsHigh correlation
Price Charged is highly correlated with KM Travelled and 1 other fieldsHigh correlation
Cost of Trip is highly correlated with KM Travelled and 1 other fieldsHigh correlation
Customer ID is highly correlated with Population and 2 other fieldsHigh correlation
Population is highly correlated with Customer ID and 2 other fieldsHigh correlation
KM Travelled is highly correlated with Price Charged and 1 other fieldsHigh correlation
Date of Travel is highly correlated with Transaction IDHigh correlation
Price Charged is highly correlated with KM Travelled and 1 other fieldsHigh correlation
City is highly correlated with Customer ID and 2 other fieldsHigh correlation
Cost of Trip is highly correlated with KM Travelled and 1 other fieldsHigh correlation
Users is highly correlated with Customer ID and 2 other fieldsHigh correlation
Transaction ID is highly correlated with Date of TravelHigh correlation
Population is highly correlated with City and 1 other fieldsHigh correlation
City is highly correlated with Population and 1 other fieldsHigh correlation
Users is highly correlated with Population and 1 other fieldsHigh correlation
Date of Travel has 80707 (18.3%) missing values Missing
Company has 80707 (18.3%) missing values Missing
City has 80706 (18.3%) missing values Missing
KM Travelled has 80707 (18.3%) missing values Missing
Price Charged has 80707 (18.3%) missing values Missing
Cost of Trip has 80707 (18.3%) missing values Missing
Population has 80706 (18.3%) missing values Missing
Users has 80706 (18.3%) missing values Missing
Transaction ID is uniformly distributed Uniform

Reproduction

Analysis started2021-06-28 05:57:46.076348
Analysis finished2021-06-28 05:59:23.552770
Duration1 minute and 37.48 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Transaction ID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct440098
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean10220059.5
Minimum10000011
Maximum10440108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2021-06-28T00:59:24.300321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum10000011
5-th percentile10022015.85
Q110110035.25
median10220059.5
Q310330083.75
95-th percentile10418103.15
Maximum10440108
Range440097
Interquartile range (IQR)220048.5

Descriptive statistics

Standard deviation127045.4937
Coefficient of variation (CV)0.01243099355
Kurtosis-1.2
Mean10220059.5
Median Absolute Deviation (MAD)110024.5
Skewness1.475091901 × 10-17
Sum4.497827746 × 1012
Variance1.614055748 × 1010
MonotonicityNot monotonic
2021-06-28T00:59:24.620979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100884721
 
< 0.1%
102481951
 
< 0.1%
101459451
 
< 0.1%
103610101
 
< 0.1%
103442531
 
< 0.1%
100165431
 
< 0.1%
101741581
 
< 0.1%
102566371
 
< 0.1%
102849311
 
< 0.1%
101847161
 
< 0.1%
Other values (440088)440088
> 99.9%
ValueCountFrequency (%)
100000111
< 0.1%
100000121
< 0.1%
100000131
< 0.1%
100000141
< 0.1%
100000151
< 0.1%
100000161
< 0.1%
100000171
< 0.1%
100000181
< 0.1%
100000191
< 0.1%
100000201
< 0.1%
ValueCountFrequency (%)
104401081
< 0.1%
104401071
< 0.1%
104401061
< 0.1%
104401051
< 0.1%
104401041
< 0.1%
104401031
< 0.1%
104401021
< 0.1%
104401011
< 0.1%
104401001
< 0.1%
104400991
< 0.1%

Date of Travel
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1095
Distinct (%)0.3%
Missing80707
Missing (%)18.3%
Infinite0
Infinite (%)0.0%
Mean42964.068
Minimum42371
Maximum43465
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2021-06-28T00:59:24.780721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum42371
5-th percentile42465
Q142697
median42988
Q343232
95-th percentile43429
Maximum43465
Range1094
Interquartile range (IQR)535

Descriptive statistics

Standard deviation307.467197
Coefficient of variation (CV)0.007156380002
Kurtosis-1.137362913
Mean42964.068
Median Absolute Deviation (MAD)273
Skewness-0.06800364811
Sum1.544094233 × 1010
Variance94536.07725
MonotonicityNot monotonic
2021-06-28T00:59:24.932352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
431052022
 
0.5%
430841123
 
0.3%
430771100
 
0.2%
434491086
 
0.2%
430631085
 
0.2%
434561084
 
0.2%
434481076
 
0.2%
430911042
 
0.2%
434281037
 
0.2%
430791032
 
0.2%
Other values (1085)347705
79.0%
(Missing)80707
 
18.3%
ValueCountFrequency (%)
42371181
< 0.1%
42372178
< 0.1%
4237325
 
< 0.1%
4237447
 
< 0.1%
42375109
 
< 0.1%
42376141
< 0.1%
42377111
 
< 0.1%
42378289
0.1%
42379272
0.1%
4238085
 
< 0.1%
ValueCountFrequency (%)
43465256
 
0.1%
43464257
 
0.1%
43463825
0.2%
43462843
0.2%
43461318
 
0.1%
43460270
 
0.1%
43459284
 
0.1%
43458279
 
0.1%
43457339
 
0.1%
434561084
0.2%

Company
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing80707
Missing (%)18.3%
Memory size6.7 MiB
Yellow Cab
274681 
Pink Cab
84711 

Length

Max length10
Median length10
Mean length9.528587169
Min length8

Characters and Unicode

Total characters3424498
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPink Cab
2nd rowYellow Cab
3rd rowYellow Cab
4th rowPink Cab
5th rowYellow Cab

Common Values

ValueCountFrequency (%)
Yellow Cab274681
62.4%
Pink Cab84711
 
19.2%
(Missing)80707
 
18.3%

Length

2021-06-28T00:59:25.303359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-28T00:59:25.392910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
cab359392
50.0%
yellow274681
38.2%
pink84711
 
11.8%

Most occurring characters

ValueCountFrequency (%)
l549362
16.0%
359392
10.5%
C359392
10.5%
a359392
10.5%
b359392
10.5%
Y274681
8.0%
e274681
8.0%
o274681
8.0%
w274681
8.0%
P84711
 
2.5%
Other values (3)254133
7.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2346322
68.5%
Uppercase Letter718784
 
21.0%
Space Separator359392
 
10.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l549362
23.4%
a359392
15.3%
b359392
15.3%
e274681
11.7%
o274681
11.7%
w274681
11.7%
i84711
 
3.6%
n84711
 
3.6%
k84711
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
C359392
50.0%
Y274681
38.2%
P84711
 
11.8%
Space Separator
ValueCountFrequency (%)
359392
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3065106
89.5%
Common359392
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
l549362
17.9%
C359392
11.7%
a359392
11.7%
b359392
11.7%
Y274681
9.0%
e274681
9.0%
o274681
9.0%
w274681
9.0%
P84711
 
2.8%
i84711
 
2.8%
Other values (2)169422
 
5.5%
Common
ValueCountFrequency (%)
359392
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3424498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l549362
16.0%
359392
10.5%
C359392
10.5%
a359392
10.5%
b359392
10.5%
Y274681
8.0%
e274681
8.0%
o274681
8.0%
w274681
8.0%
P84711
 
2.5%
Other values (3)254133
7.4%

City
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct20
Distinct (%)< 0.1%
Missing80706
Missing (%)18.3%
Memory size6.7 MiB
NEW YORK NY
99885 
CHICAGO IL
56625 
LOS ANGELES CA
48033 
WASHINGTON DC
43737 
BOSTON MA
29692 
Other values (15)
81421 

Length

Max length16
Median length11
Mean length11.29947717
Min length8

Characters and Unicode

Total characters4060953
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowATLANTA GA
2nd rowATLANTA GA
3rd rowATLANTA GA
4th rowATLANTA GA
5th rowATLANTA GA

Common Values

ValueCountFrequency (%)
NEW YORK NY99885
22.7%
CHICAGO IL56625
12.9%
LOS ANGELES CA48033
10.9%
WASHINGTON DC43737
9.9%
BOSTON MA29692
 
6.7%
SAN DIEGO CA20488
 
4.7%
SILICON VALLEY8519
 
1.9%
SEATTLE WA7997
 
1.8%
ATLANTA GA7557
 
1.7%
DALLAS TX7017
 
1.6%
Other values (10)29843
 
6.8%
(Missing)80706
18.3%

Length

2021-06-28T00:59:25.616232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new99885
11.3%
ny99885
11.3%
york99885
11.3%
ca70889
 
8.0%
il56625
 
6.4%
chicago56625
 
6.4%
angeles48033
 
5.4%
los48033
 
5.4%
dc43737
 
4.9%
washington43737
 
4.9%
Other values (28)219859
24.8%

Most occurring characters

ValueCountFrequency (%)
527800
13.0%
N430602
10.6%
A366625
 
9.0%
O354823
 
8.7%
E260025
 
6.4%
C248502
 
6.1%
S227035
 
5.6%
L220310
 
5.4%
I218705
 
5.4%
Y212271
 
5.2%
Other values (15)994255
24.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3533153
87.0%
Space Separator527800
 
13.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N430602
12.2%
A366625
10.4%
O354823
10.0%
E260025
 
7.4%
C248502
 
7.0%
S227035
 
6.4%
L220310
 
6.2%
I218705
 
6.2%
Y212271
 
6.0%
G181735
 
5.1%
Other values (14)812520
23.0%
Space Separator
ValueCountFrequency (%)
527800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3533153
87.0%
Common527800
 
13.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N430602
12.2%
A366625
10.4%
O354823
10.0%
E260025
 
7.4%
C248502
 
7.0%
S227035
 
6.4%
L220310
 
6.2%
I218705
 
6.2%
Y212271
 
6.0%
G181735
 
5.1%
Other values (14)812520
23.0%
Common
ValueCountFrequency (%)
527800
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4060953
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
527800
13.0%
N430602
10.6%
A366625
 
9.0%
O354823
 
8.7%
E260025
 
6.4%
C248502
 
6.1%
S227035
 
5.6%
L220310
 
5.4%
I218705
 
5.4%
Y212271
 
5.2%
Other values (15)994255
24.5%

KM Travelled
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct874
Distinct (%)0.2%
Missing80707
Missing (%)18.3%
Infinite0
Infinite (%)0.0%
Mean22.56725408
Minimum1.9
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2021-06-28T00:59:25.757355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile3.57
Q112
median22.44
Q332.96
95-th percentile42
Maximum48
Range46.1
Interquartile range (IQR)20.96

Descriptive statistics

Standard deviation12.23352593
Coefficient of variation (CV)0.5420919125
Kurtosis-1.126875356
Mean22.56725408
Median Absolute Deviation (MAD)10.45
Skewness0.05577890774
Sum8110490.58
Variance149.6591566
MonotonicityNot monotonic
2021-06-28T00:59:25.932116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.61536
 
0.3%
241080
 
0.2%
22.81075
 
0.2%
35.71069
 
0.2%
16.81065
 
0.2%
37.441062
 
0.2%
39.61056
 
0.2%
28.08972
 
0.2%
21.85769
 
0.2%
18754
 
0.2%
Other values (864)348954
79.3%
(Missing)80707
 
18.3%
ValueCountFrequency (%)
1.9339
0.1%
1.92375
0.1%
1.94329
0.1%
1.96383
0.1%
1.98374
0.1%
2362
0.1%
2.02341
0.1%
2.04358
0.1%
2.06346
0.1%
2.08369
0.1%
ValueCountFrequency (%)
48366
0.1%
47.6381
0.1%
47.2378
0.1%
46.8737
0.2%
46.41380
0.1%
46.4356
0.1%
46.02385
0.1%
46336
0.1%
45.63344
0.1%
45.6704
0.2%

Price Charged
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct99176
Distinct (%)27.6%
Missing80707
Missing (%)18.3%
Infinite0
Infinite (%)0.0%
Mean423.4433113
Minimum15.6
Maximum2048.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2021-06-28T00:59:26.087487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum15.6
5-th percentile63.42
Q1206.4375
median386.36
Q3583.66
95-th percentile944.89
Maximum2048.03
Range2032.43
Interquartile range (IQR)377.2225

Descriptive statistics

Standard deviation274.3789114
Coefficient of variation (CV)0.6479708243
Kurtosis0.7476354732
Mean423.4433113
Median Absolute Deviation (MAD)187.22
Skewness0.8737614916
Sum152182138.5
Variance75283.78705
MonotonicityNot monotonic
2021-06-28T00:59:26.336790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
191.2718
 
< 0.1%
298.3218
 
< 0.1%
216.3717
 
< 0.1%
198.817
 
< 0.1%
181.5917
 
< 0.1%
115.5317
 
< 0.1%
79.3816
 
< 0.1%
260.0915
 
< 0.1%
367.6815
 
< 0.1%
264.8315
 
< 0.1%
Other values (99166)359227
81.6%
(Missing)80707
 
18.3%
ValueCountFrequency (%)
15.61
< 0.1%
15.751
< 0.1%
16.381
< 0.1%
16.531
< 0.1%
16.761
< 0.1%
17.031
< 0.1%
17.111
< 0.1%
17.211
< 0.1%
17.271
< 0.1%
17.461
< 0.1%
ValueCountFrequency (%)
2048.031
< 0.1%
2016.71
< 0.1%
2013.951
< 0.1%
1993.831
< 0.1%
1981.051
< 0.1%
1978.791
< 0.1%
1957.11
< 0.1%
1947.911
< 0.1%
1925.921
< 0.1%
1920.591
< 0.1%

Cost of Trip
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct16291
Distinct (%)4.5%
Missing80707
Missing (%)18.3%
Infinite0
Infinite (%)0.0%
Mean286.1901128
Minimum19
Maximum691.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2021-06-28T00:59:26.504947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile46.224
Q1151.2
median282.48
Q3413.6832
95-th percentile544.3632
Maximum691.2
Range672.2
Interquartile range (IQR)262.4832

Descriptive statistics

Standard deviation157.9936612
Coefficient of variation (CV)0.5520584188
Kurtosis-1.012232752
Mean286.1901128
Median Absolute Deviation (MAD)131.232
Skewness0.1379580609
Sum102854437
Variance24961.99696
MonotonicityNot monotonic
2021-06-28T00:59:26.653291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
362.88186
 
< 0.1%
479.808184
 
< 0.1%
471.744180
 
< 0.1%
205.632178
 
< 0.1%
411.264166
 
< 0.1%
336.96166
 
< 0.1%
428.4164
 
< 0.1%
423.36161
 
< 0.1%
241.92161
 
< 0.1%
443.52160
 
< 0.1%
Other values (16281)357686
81.3%
(Missing)80707
 
18.3%
ValueCountFrequency (%)
192
< 0.1%
19.194
< 0.1%
19.24
< 0.1%
19.382
< 0.1%
19.3921
 
< 0.1%
19.43
< 0.1%
19.573
< 0.1%
19.5842
< 0.1%
19.5943
< 0.1%
19.63
< 0.1%
ValueCountFrequency (%)
691.29
 
< 0.1%
685.4429
< 0.1%
679.72814
 
< 0.1%
679.6833
< 0.1%
674.01634
< 0.1%
673.9236
< 0.1%
668.35215
 
< 0.1%
668.30449
< 0.1%
668.1621
< 0.1%
662.734819
 
< 0.1%

Customer ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct49171
Distinct (%)11.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean23619.51312
Minimum1
Maximum60000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2021-06-28T00:59:26.812587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile664
Q13530
median15168
Q343884
95-th percentile57784
Maximum60000
Range59999
Interquartile range (IQR)40354

Descriptive statistics

Standard deviation21195.54982
Coefficient of variation (CV)0.897374544
Kurtosis-1.560810014
Mean23619.51312
Median Absolute Deviation (MAD)14002
Skewness0.341134246
Sum1.039490048 × 1010
Variance449251332
MonotonicityNot monotonic
2021-06-28T00:59:26.969187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49454
 
< 0.1%
293953
 
< 0.1%
276651
 
< 0.1%
107051
 
< 0.1%
253950
 
< 0.1%
90350
 
< 0.1%
180350
 
< 0.1%
94450
 
< 0.1%
106750
 
< 0.1%
85850
 
< 0.1%
Other values (49161)439589
99.9%
ValueCountFrequency (%)
129
< 0.1%
240
< 0.1%
346
< 0.1%
426
< 0.1%
531
< 0.1%
628
< 0.1%
736
< 0.1%
835
< 0.1%
940
< 0.1%
1024
< 0.1%
ValueCountFrequency (%)
6000018
< 0.1%
599998
< 0.1%
599989
< 0.1%
5999710
< 0.1%
599964
 
< 0.1%
5999513
< 0.1%
5999413
< 0.1%
5999313
< 0.1%
5999211
< 0.1%
599919
< 0.1%

Payment_Mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size6.7 MiB
Card
263991 
Cash
176107 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1760392
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCard
2nd rowCash
3rd rowCard
4th rowCard
5th rowCard

Common Values

ValueCountFrequency (%)
Card263991
60.0%
Cash176107
40.0%
(Missing)1
 
< 0.1%

Length

2021-06-28T00:59:27.223650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-28T00:59:27.298318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
card263991
60.0%
cash176107
40.0%

Most occurring characters

ValueCountFrequency (%)
C440098
25.0%
a440098
25.0%
r263991
15.0%
d263991
15.0%
s176107
10.0%
h176107
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1320294
75.0%
Uppercase Letter440098
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a440098
33.3%
r263991
20.0%
d263991
20.0%
s176107
13.3%
h176107
13.3%
Uppercase Letter
ValueCountFrequency (%)
C440098
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1760392
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C440098
25.0%
a440098
25.0%
r263991
15.0%
d263991
15.0%
s176107
10.0%
h176107
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1760392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C440098
25.0%
a440098
25.0%
r263991
15.0%
d263991
15.0%
s176107
10.0%
h176107
10.0%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size6.7 MiB
Male
256611 
Female
183487 

Length

Max length6
Median length4
Mean length4.833846098
Min length4

Characters and Unicode

Total characters2127366
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male256611
58.3%
Female183487
41.7%
(Missing)1
 
< 0.1%

Length

2021-06-28T00:59:27.523055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-28T00:59:27.611001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
male256611
58.3%
female183487
41.7%

Most occurring characters

ValueCountFrequency (%)
e623585
29.3%
a440098
20.7%
l440098
20.7%
M256611
12.1%
F183487
 
8.6%
m183487
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1687268
79.3%
Uppercase Letter440098
 
20.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e623585
37.0%
a440098
26.1%
l440098
26.1%
m183487
 
10.9%
Uppercase Letter
ValueCountFrequency (%)
M256611
58.3%
F183487
41.7%

Most occurring scripts

ValueCountFrequency (%)
Latin2127366
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e623585
29.3%
a440098
20.7%
l440098
20.7%
M256611
12.1%
F183487
 
8.6%
m183487
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2127366
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e623585
29.3%
a440098
20.7%
l440098
20.7%
M256611
12.1%
F183487
 
8.6%
m183487
 
8.6%

Age
Real number (ℝ≥0)

Distinct48
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean35.36019705
Minimum18
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2021-06-28T00:59:27.704114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile19
Q125
median33
Q342
95-th percentile61
Maximum65
Range47
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.58266805
Coefficient of variation (CV)0.3558427016
Kurtosis-0.4603646408
Mean35.36019705
Median Absolute Deviation (MAD)8
Skewness0.6819975877
Sum15561952
Variance158.3235351
MonotonicityNot monotonic
2021-06-28T00:59:27.856529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
2315005
 
3.4%
2014997
 
3.4%
3914591
 
3.3%
3214431
 
3.3%
2514407
 
3.3%
2214334
 
3.3%
3014287
 
3.2%
2714235
 
3.2%
4014210
 
3.2%
2614125
 
3.2%
Other values (38)295476
67.1%
ValueCountFrequency (%)
1813572
3.1%
1913941
3.2%
2014997
3.4%
2113507
3.1%
2214334
3.3%
2315005
3.4%
2413916
3.2%
2514407
3.3%
2614125
3.2%
2714235
3.2%
ValueCountFrequency (%)
654065
0.9%
644760
1.1%
634532
1.0%
624458
1.0%
615207
1.2%
604597
1.0%
594978
1.1%
584995
1.1%
574380
1.0%
564583
1.0%

Income (USD/Month)
Real number (ℝ≥0)

Distinct23341
Distinct (%)5.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean15092.18199
Minimum2000
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2021-06-28T00:59:28.007817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile3244
Q18391
median14767
Q321084
95-th percentile29659
Maximum35000
Range33000
Interquartile range (IQR)12693

Descriptive statistics

Standard deviation7987.309505
Coefficient of variation (CV)0.5292349052
Kurtosis-0.6753031806
Mean15092.18199
Median Absolute Deviation (MAD)6337
Skewness0.2999555469
Sum6642039109
Variance63797113.13
MonotonicityNot monotonic
2021-06-28T00:59:28.361444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20884165
 
< 0.1%
8756135
 
< 0.1%
8899133
 
< 0.1%
6808133
 
< 0.1%
17468131
 
< 0.1%
24826130
 
< 0.1%
22525129
 
< 0.1%
3878126
 
< 0.1%
7070123
 
< 0.1%
8518122
 
< 0.1%
Other values (23331)438771
99.7%
ValueCountFrequency (%)
20009
 
< 0.1%
20011
 
< 0.1%
20022
 
< 0.1%
20039
 
< 0.1%
20046
 
< 0.1%
200727
 
< 0.1%
20092
 
< 0.1%
2010104
< 0.1%
201110
 
< 0.1%
201275
< 0.1%
ValueCountFrequency (%)
350001
 
< 0.1%
3499615
< 0.1%
349954
 
< 0.1%
3498930
< 0.1%
3498516
< 0.1%
3498423
< 0.1%
349833
 
< 0.1%
349791
 
< 0.1%
349772
 
< 0.1%
349732
 
< 0.1%

Population
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct20
Distinct (%)< 0.1%
Missing80706
Missing (%)18.3%
Memory size6.7 MiB
8,405,837
99885 
1,955,130
56625 
1,595,037
48033 
418,859
43737 
248,968
29692 
Other values (15)
81421 

Length

Max length11
Median length11
Mean length10.24375266
Min length9

Characters and Unicode

Total characters3681533
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row 814,885
2nd row 814,885
3rd row 814,885
4th row 814,885
5th row 814,885

Common Values

ValueCountFrequency (%)
8,405,837 99885
22.7%
1,955,130 56625
12.9%
1,595,037 48033
10.9%
418,859 43737
9.9%
248,968 29692
 
6.7%
959,307 20488
 
4.7%
1,177,609 8519
 
1.9%
671,238 7997
 
1.8%
814,885 7557
 
1.7%
942,908 7017
 
1.6%
Other values (10)29843
 
6.8%
(Missing)80706
18.3%

Length

2021-06-28T00:59:28.643662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8,405,83799885
27.8%
1,955,13056625
15.8%
1,595,03748033
13.4%
418,85943737
12.2%
248,96829692
 
8.3%
959,30720488
 
5.7%
1,177,6098519
 
2.4%
671,2387997
 
2.2%
814,8857557
 
2.1%
942,9087017
 
2.0%
Other values (10)29843
 
8.3%

Most occurring characters

ValueCountFrequency (%)
718786
19.5%
,582891
15.8%
5412069
11.2%
8394504
10.7%
3269469
 
7.3%
1265312
 
7.2%
9261224
 
7.1%
0249844
 
6.8%
7209906
 
5.7%
4201319
 
5.5%
Other values (2)116209
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2379856
64.6%
Space Separator718786
 
19.5%
Other Punctuation582891
 
15.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5412069
17.3%
8394504
16.6%
3269469
11.3%
1265312
11.1%
9261224
11.0%
0249844
10.5%
7209906
8.8%
4201319
8.5%
260806
 
2.6%
655403
 
2.3%
Space Separator
ValueCountFrequency (%)
718786
100.0%
Other Punctuation
ValueCountFrequency (%)
,582891
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3681533
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
718786
19.5%
,582891
15.8%
5412069
11.2%
8394504
10.7%
3269469
 
7.3%
1265312
 
7.2%
9261224
 
7.1%
0249844
 
6.8%
7209906
 
5.7%
4201319
 
5.5%
Other values (2)116209
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3681533
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
718786
19.5%
,582891
15.8%
5412069
11.2%
8394504
10.7%
3269469
 
7.3%
1265312
 
7.2%
9261224
 
7.1%
0249844
 
6.8%
7209906
 
5.7%
4201319
 
5.5%
Other values (2)116209
 
3.2%

Users
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct20
Distinct (%)< 0.1%
Missing80706
Missing (%)18.3%
Memory size6.7 MiB
302,149
99885 
164,468
56625 
144,132
48033 
127,001
43737 
80,021
29692 
Other values (15)
81421 

Length

Max length9
Median length9
Mean length8.661103583
Min length7

Characters and Unicode

Total characters3112740
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row 24,701
2nd row 24,701
3rd row 24,701
4th row 24,701
5th row 24,701

Common Values

ValueCountFrequency (%)
302,149 99885
22.7%
164,468 56625
12.9%
144,132 48033
10.9%
127,001 43737
9.9%
80,021 29692
 
6.7%
69,995 20488
 
4.7%
27,247 8519
 
1.9%
25,063 7997
 
1.8%
24,701 7557
 
1.7%
22,157 7017
 
1.6%
Other values (10)29843
 
6.8%
(Missing)80706
18.3%

Length

2021-06-28T00:59:28.908199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
302,14999885
27.8%
164,46856625
15.8%
144,13248033
13.4%
127,00143737
12.2%
80,02129692
 
8.3%
69,99520488
 
5.7%
27,2478519
 
2.4%
25,0637997
 
2.2%
24,7017557
 
2.1%
22,1577017
 
2.0%
Other values (10)29843
 
8.3%

Most occurring characters

ValueCountFrequency (%)
718786
23.1%
1411294
13.2%
,359393
11.5%
4344027
11.1%
2284547
 
9.1%
0267675
 
8.6%
9177220
 
5.7%
3162670
 
5.2%
6151567
 
4.9%
7100461
 
3.2%
Other values (2)135100
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2034561
65.4%
Space Separator718786
 
23.1%
Other Punctuation359393
 
11.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1411294
20.2%
4344027
16.9%
2284547
14.0%
0267675
13.2%
9177220
8.7%
3162670
 
8.0%
6151567
 
7.4%
7100461
 
4.9%
891213
 
4.5%
543887
 
2.2%
Space Separator
ValueCountFrequency (%)
718786
100.0%
Other Punctuation
ValueCountFrequency (%)
,359393
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3112740
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
718786
23.1%
1411294
13.2%
,359393
11.5%
4344027
11.1%
2284547
 
9.1%
0267675
 
8.6%
9177220
 
5.7%
3162670
 
5.2%
6151567
 
4.9%
7100461
 
3.2%
Other values (2)135100
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3112740
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
718786
23.1%
1411294
13.2%
,359393
11.5%
4344027
11.1%
2284547
 
9.1%
0267675
 
8.6%
9177220
 
5.7%
3162670
 
5.2%
6151567
 
4.9%
7100461
 
3.2%
Other values (2)135100
 
4.3%

Interactions

2021-06-28T00:58:55.272345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:58:57.110078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:58:57.304232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:58:57.496252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:58:57.759018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:58:58.023431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:58:58.230121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:58:58.425163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:58:58.645157image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:58:58.833405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:58:59.040678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:58:59.241515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:58:59.470512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:01.756284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:01.959210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:02.145761image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:02.443613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:02.630703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:02.817715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:03.004803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:03.202260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:03.390657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:03.577075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:03.756198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:03.946473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:04.147105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:04.349997image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:04.599173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:04.808693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:05.008924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:05.208550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:05.398636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:05.604347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:05.799158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:05.995863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:06.190956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:06.394388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:06.590764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:06.786378image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:06.973190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:07.187112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:07.490821image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:07.684522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:07.875238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:08.076850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:08.276708image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:08.475744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:08.667152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:08.874908image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:09.055541image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:09.238018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:09.419242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:09.611956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:09.805146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:09.993243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:10.175707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:10.375824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:10.574901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:10.768484image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:10.961449image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:11.163948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:11.366447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:11.567439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T00:59:11.759060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-06-28T00:59:29.157526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-28T00:59:29.335876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-28T00:59:29.564144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-28T00:59:29.754955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-06-28T00:59:29.956242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-06-28T00:59:12.319457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-28T00:59:13.608567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-06-28T00:59:20.945060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-06-28T00:59:22.247169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Transaction IDDate of TravelCompanyCityKM TravelledPrice ChargedCost of TripCustomer IDPayment_ModeGenderAgeIncome (USD/Month)PopulationUsers
010000011.042377.0Pink CabATLANTA GA30.45370.95313.635029290.0CardMale28.010813.0814,88524,701
110351127.043302.0Yellow CabATLANTA GA26.19598.70317.422829290.0CashMale28.010813.0814,88524,701
210412921.043427.0Yellow CabATLANTA GA42.55792.05597.402029290.0CardMale28.010813.0814,88524,701
310000012.042375.0Pink CabATLANTA GA28.62358.52334.854027703.0CardMale27.09237.0814,88524,701
410320494.043211.0Yellow CabATLANTA GA36.38721.10467.119227703.0CardMale27.09237.0814,88524,701
510324737.043224.0Yellow CabATLANTA GA6.18138.4087.508827703.0CashMale27.09237.0814,88524,701
610395626.043400.0Pink CabATLANTA GA13.39167.03141.934027703.0CardMale27.09237.0814,88524,701
710000013.042371.0Pink CabATLANTA GA9.04125.2097.632028712.0CashMale53.011242.0814,88524,701
810079404.042634.0Yellow CabATLANTA GA39.60704.30494.208028712.0CardMale53.011242.0814,88524,701
910186994.042909.0Yellow CabATLANTA GA18.19365.63246.656428712.0CardMale53.011242.0814,88524,701

Last rows

Transaction IDDate of TravelCompanyCityKM TravelledPrice ChargedCost of TripCustomer IDPayment_ModeGenderAgeIncome (USD/Month)PopulationUsers
44008910274704.043075.0Yellow CabWASHINGTON DC42.80627.21559.824052614.0CardFemale44.08303.0418,859127,001
44009010311299.043174.0Yellow CabWASHINGTON DC13.56241.43165.974452614.0CardFemale44.08303.0418,859127,001
44009110439949.043102.0Yellow CabWASHINGTON DC34.80507.12484.416052614.0CashFemale44.08303.0418,859127,001
44009210284072.043086.0Yellow CabWASHINGTON DC44.00679.97607.200051406.0CashFemale29.06829.0418,859127,001
44009310307228.043162.0Yellow CabWASHINGTON DC38.40668.93525.312051406.0CashFemale29.06829.0418,859127,001
44009410319775.043203.0Yellow CabWASHINGTON DC3.5767.6044.553651406.0CashFemale29.06829.0418,859127,001
44009510347676.043287.0Yellow CabWASHINGTON DC23.46331.97337.824051406.0CardFemale29.06829.0418,859127,001
44009610358624.043314.0Yellow CabWASHINGTON DC27.60358.23364.320051406.0CashFemale29.06829.0418,859127,001
44009710370709.043342.0Yellow CabWASHINGTON DC34.24453.11427.315251406.0CardFemale29.06829.0418,859127,001
440098NaNNaNNaNSAN FRANCISCO CANaNNaNNaNNaNNaNNaNNaNNaN629,591213,609